Machine Learning Models for Selecting Treatments for Treating an Agricultural Field

1. A system for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; selecting at least one specific weed parameter of the plurality of weed parameters according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed parameter being depicted in the input image; wherein the selecting is performed when an accuracy of classification of the machine learning model for at least one certain weed parameter is above a threshold; wherein the specific weed parameter and the non-specific weed parameter are of a same species of weed of different sizes during different growth stages, wherein test images depict same weed species of various sizes and/or various growth stages, wherein the threshold is set to differentiate between weeds depicted in input images that are of growth stages above a size threshold and weeds depicted in the image that are of other growth stages below the size threshold.

2. A system for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; selecting at least one specific weed parameter of the plurality of weed parameters according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed parameter being depicted in the input image; wherein the selecting comprises is performed when an accuracy of classification of the machine learning model for at least one certain weed parameter is above a threshold; wherein the machine learning model comprises a detector component, wherein the test images depict weeds that are of various visual similarities to a ground, the threshold is set to differentiate detection of weeds depicted in the input image that are visually non-similar to the ground and weeds depicted in the input image that are visually similar to the ground.

3. A system for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; selecting at least one specific weed parameter of the plurality of weed parameters according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed parameter being depicted in the input image; wherein the selecting is performed when an accuracy of classification of the machine learning model for at least one certain weed parameter is above a threshold; wherein the machine learning model comprises a classifier component, wherein the test images depict weeds that are of various visual similarities to a desired crop, the threshold is set to differentiate classification of weeds that are visually similar to the desired crop from weeds that are visually non-similar to the desired crop.

4. The system of claim 3 , wherein setting up instructions for triggering application of the first herbicide comprises setting up instructions for triggering application of the first herbicide using a spot treatment application element designed to apply treatment to a specific spot depicted in the input image, and setting up instructions for triggering application of the second herbicide comprises setting up instructions for triggering application of the second herbicide using a broadcast treatment application element designed to apply treatment using a broadcast approach to a broad region.

5. The system of claim 3 , wherein the first herbicide and the second herbicide are liquid chemicals stored in respective containers on an agricultural machine that includes treatment application elements for application to the target agricultural field.

6. The system of claim 3 , wherein the second herbicide is further designed to treat weeds having weed parameters of a plurality of species of weeds prior to sprouting from the ground, and/or small weeds less than a size threshold.

7. The system of claim 3 , wherein the machine learning model comprises a detector component that generates an outcome of boxes in response to the input image, each box representing a respective weed having at least one weed parameter depicted therein, the detector component trained on a training dataset of sample images labelled with ground truth sample boxes each depicting a sample weed having at least one weed parameter therein.

8. The system of claim 3 , wherein the plurality of weed parameters are selected from a group consisting of: weed species, and growth stage.

9. The system of claim 3 , wherein the training dataset includes the plurality of sample images of the at least one agricultural field further labelled with ground truth of a plurality of field parameters of the corresponding sample agricultural field, and the plurality of test images are fed into the machine learning model with at least one field parameter of the target agricultural field.

10. The system of claim 9 , wherein the plurality of field parameters are selected from a group consisting of: geographical location, season, phase during an agricultural growth cycle, soil type, whether soil is tilled, whether soil is untitled, weather, and desired crop being grown.

11. The system of claim 3 , wherein the at least one hardware processor further executes a code for: in a plurality of iterations, while maneuvering over a plurality of portions of the target agricultural field, for each respective portion of the target agricultural field: accessing a respective input image depicting the respective portion of the target agricultural field, the respective input image captured by an imaging sensor located on an agricultural machine; feeding the respective input image into the machine learning model; analyzing an outcome of the machine learning model to determine likelihood of the at least one specific weed parameter being depicted in the respective input image; in response to the at least one specific weed parameter likely being depicted in the respective input image, instructing application of the first herbicide to the respective portion of the target agricultural field depicted in the input image; and in response to the at least one specific weed parameter non-likely being depicted in the respective input image, instructing application of the second herbicide to the respective portion of the target agricultural field depicted in the input image.

12. The system of claim 11 , wherein the agricultural machine is connected to a spray boom, wherein at least one treatment application element for application of the first herbicide and the second herbicide and, the imaging sensor are connected to the spray boom.

13. The system of claim 3 , wherein the second herbicide is a broad herbicide selected for treating weeds having a subset of the plurality of weed parameters that exclude the at least one specific weed parameter.

14. The system of claim 3 , wherein the first herbicide comprises a specific herbicide selected for treating weeds having the at least one specific weed parameter.

15. The system of claim 3 , wherein the test images are captured by an imaging sensor at a resolution corresponding to a target resolution of a target imaging sensor that captures the input image.

16. The system of claim 3 , wherein a set of weed parameters of the plurality of weed parameters are designated as non-specific weed parameters when the accuracy of classification of the machine learning model is below the threshold.

17. A system for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; selecting at least one specific weed parameter of the plurality of weed parameters according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed parameter being depicted in the input image; wherein the machine learning model comprises a classifier component that generates an outcome of probability of the at least one weed parameter being depicted in the image, the classifier component trained on a training dataset of sample images tagged with a ground truth label indicating presence or absence of sample weeds having weed parameters depicted therein.

18. A system for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; selecting at least one specific weed parameter of the plurality of weed parameters according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed parameter being depicted in the input image; wherein the machine learning model is implemented as: a detector component trained on a training dataset of images annotated with ground truth boundaries indicating respective objects associated with respective weed parameters, is fed an input image, for generating an outcome of a plurality of bounding boxes, each respective bounding box is associated with a respective first probability value indicating likelihood of a respective weed parameter(s) being depicted in the respective box, for a first subset of bounding boxes associated with the respective first probability values less than a first threshold, respective patches corresponding to the subset are extracted from the image, wherein a second subset of bounding boxes are associated with respective first probability values greater than the first threshold; the extracted respective patches are fed into a classifier component trained on a training dataset of patches extracted from images labelled with ground truth labels indicating respective weed parameters, for obtaining a second probability value indicating likelihood of a respective weed parameter(s) being depicted in the respective patch, selecting a third subset of bounding boxes from the first subset according to respective second probability value greater than the first threshold; clustering the second subset and the third subset according to respective weed parameter(s); and computing a respective third probability value for each weed parameter of each cluster, wherein the respective third probability denoted likelihood of the at least one specific weed parameter being depicted in an input image of the portion of the target agricultural field used to trigger the instructions for application of the first herbicide or the second herbicide.

19. A method for customizing a computing device for dynamic application of herbicides to a target agricultural field, comprising: feeding a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one agricultural field labelled with ground truth of a plurality of weed types; selecting at least one specific weed type of the plurality of weed types according to at least one performance metric of the machine learning model; setting up instructions for triggering application of a specific herbicide to a portion of the target agricultural field in response to an outcome of the machine learning model indicating likelihood of the at least one specific weed type being depicted in an input image of the portion of the target agricultural field; and setting up instructions for triggering application of a non-specific herbicide to the portion of the target agricultural field in response to the outcome of the machine learning model indicating non-likelihood of the at least one specific weed type being depicted in the input image; wherein the selecting is performed when an accuracy of classification of the machine learning model for at least one certain weed parameter is above a threshold; wherein the machine learning model comprises a classifier component, wherein the test images depict weeds that are of various visual similarities to a desired crop, the threshold is set to differentiate classification of weeds that are visually similar to the desired crop from weeds that are visually non-similar to the desired crop.

20. A system for customized dynamic application of herbicides to a target agricultural field, comprising: at least one hardware processor executing a code for: in a plurality of iterations, while maneuvering over a plurality of portions of the target agricultural field, for each respective portion of the target agricultural field: accessing a respective input image depicting the respective portion of the target agricultural field, the respective input image captured by an imaging sensor located on an agricultural machine; feeding the respective input image into a machine learning model; analyzing an outcome of the machine learning model to determine likelihood of at least one specific weed parameter being depicted in the respective input image; in response to the at least one specific weed parameter likely being depicted in the respective input image, instructing application of a first herbicide to the respective portion of the target agricultural field depicted in the input image; and in response to the at least one specific weed parameter non-likely being depicted in the respective input image, instructing application of a second herbicide to the respective portion of the target agricultural field depicted in the input image, wherein the at least one specific weed parameter is selected from a plurality of weed parameters according to at least one performance metric of the machine learning model fed a plurality of test images corresponding to the target agricultural field into a machine learning model trained on a training dataset of a plurality of sample images of at least one sample agricultural field labelled with ground truth of a plurality of weed parameters; wherein the selecting is performed when an accuracy of classification of the machine learning model for at least one certain weed parameter is above a threshold; wherein the machine learning model comprises a classifier component, wherein the test images depict weeds that are of various visual similarities to a desired crop, the threshold is set to differentiate classification of weeds that are visually similar to the desired crop from weeds that are visually non-similar to the desired crop.